Multitarget pharmacology for Dystonia in M1 (Neymotin et al 2016)

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Accession:189154
" ... We developed a multiscale model of primary motor cortex, ranging from molecular, up to cellular, and network levels, containing 1715 compartmental model neurons with multiple ion channels and intracellular molecular dynamics. We wired the model based on electrophysiological data obtained from mouse motor cortex circuit mapping experiments. We used the model to reproduce patterns of heightened activity seen in dystonia by applying independent random variations in parameters to identify pathological parameter sets. ..."
Reference:
1 . Neymotin SA, Dura-Bernal S, Lakatos P, Sanger TD, Lytton WW (2016) Multitarget Multiscale Simulation for Pharmacological Treatment of Dystonia in Motor Cortex. Front Pharmacol 7:157 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Molecular Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex V1 pyramidal corticothalamic L6 cell; Neocortex U1 pyramidal intratelencephalic L2-5 cell; Neocortex V1 interneuron basket PV cell; Neocortex fast spiking (FS) interneuron; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron; Neocortex layer 4 neuron; Neocortex layer 2-3 interneuron; Neocortex layer 4 interneuron; Neocortex layer 5 interneuron; Neocortex layer 6a interneuron;
Channel(s): I A; I h; I_SERCA; Ca pump; I K,Ca; I Calcium; I L high threshold; I T low threshold; I N; I_KD; I M; I Na,t;
Gap Junctions:
Receptor(s): GabaA; GabaB; AMPA; mGluR;
Gene(s): HCN1;
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON; Python;
Model Concept(s): Oscillations; Activity Patterns; Beta oscillations; Reaction-diffusion; Calcium dynamics; Pathophysiology; Multiscale;
Implementer(s): Neymotin, Sam [samn at neurosim.downstate.edu]; Dura-Bernal, Salvador [salvadordura at gmail.com];
Search NeuronDB for information about:  Neocortex V1 pyramidal corticothalamic L6 cell; Neocortex V1 interneuron basket PV cell; Neocortex U1 pyramidal intratelencephalic L2-5 cell; GabaA; GabaB; AMPA; mGluR; I Na,t; I L high threshold; I N; I T low threshold; I A; I M; I h; I K,Ca; I Calcium; I_SERCA; I_KD; Ca pump; Gaba; Glutamate;
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dystdemo
readme.txt
cagk.mod
cal.mod *
calts.mod *
can.mod *
cat.mod *
gabab.mod
h_winograd.mod
HCN1.mod
IC.mod *
icalts.mod *
ihlts.mod *
kap.mod
kcalts.mod *
kdmc.mod
kdr.mod
km.mod *
mglur.mod *
misc.mod *
MyExp2SynBB.mod *
MyExp2SynNMDABB.mod
nax.mod
stats.mod *
vecst.mod *
aux_fun.inc *
conf.py
declist.hoc *
decnqs.hoc *
decvec.hoc *
default.hoc *
drline.hoc *
geom.py
ghk.inc *
grvec.hoc
init.hoc
labels.hoc
labels.py *
local.hoc *
misc.h
mpisim.py
netcfg.cfg
nqs.hoc *
nqs.py
nrnoc.hoc *
pyinit.py *
python.hoc *
pywrap.hoc *
simctrl.hoc *
simdat.py
syn.py
syncode.hoc *
vector.py *
xgetargs.hoc *
                            
// $Id: pywrap.hoc,v 1.31 2012/08/04 03:19:13 samn Exp $ 

//* variables
declare("INITPYWRAP",0) // whether initialized properly

//* initialize pywrap
if(2!=name_declared("p")) {
  print "pywrap.hoc: loading python.hoc"
  load_file("python.hoc")
}
func initpywrap () { localobj pjnk
  INITPYWRAP=0
  if(2!=name_declared("p")){printf("initpywrap ERR0A: PythonObject p not found in python.hoc!\n") return 0}
  print p  
  pjnk=new PythonObject()
  if(!isojt(p,pjnk)){printf("initpywrap ERR0B: PythonObject p not found in python.hoc!\n")}
  if(!nrnpython("import numpy")) {printf("pypmtm ERR0C: could not import numpy python library!\n") return 0}
  INITPYWRAP=1
  return 1
}
initpywrap()

//** pypmtm(vec,samplingrate[,nw])
// this function calls python version of pmtm, runs multitaper power spectra, returns an nqs
obfunc pypmtm () { local sampr,spc,nw localobj vin,str,nqp,ptmp
  if(!INITPYWRAP) {printf("pypmtm ERR0A: python.hoc not initialized properly\n") return nil}
  if(!nrnpython("from mtspec import *")) {printf("pypmtm ERR0B: could not import mtspec python library!\n") return nil}  
/*  if(!nrnpython("import numpy")) {printf("pypmtm ERR0C: could not import numpy python library!\n") return nil}*/
  if(numarg()==0) {printf("pypmtm(vec,samplingrate)\n") return nil}
  vin=$o1 sampr=$2 str=new String()
  p.vjnk = vin.to_python()
  p.vjnk = p.numpy.array(p.vjnk)
  spc = 1.0 / sampr // "spacing"
  nw=4 if(numarg()>2) nw=$3
  sprint(str.s,"[Pxx,w]=mtspec(vjnk,%g,%d)",spc,nw)
  nrnpython(str.s)
  nqp=new NQS("f","pow")
  nqp.v.from_python(p.w)
  nqp.v[1].from_python(p.Pxx)
  return nqp
}

//** pybspow(vec,samplingrate[,maxf,pord])
// this function calls python version of bsmart, to get power pectrum, returns an nqs
// pord is order of polynomial -- higher == less smoothing. default is 12
obfunc pybspow () { local sampr,pord,maxf localobj vin,str,nqp,ptmp
  if(!INITPYWRAP) {printf("pybspow ERR0A: python.hoc not initialized properly\n") return nil}
  if(!nrnpython("from spectrum import ar")) {printf("pybspow ERR0B: could not import spectrum python library!\n") return nil}  
  if(numarg()==0) {printf("pybspow(vec,samplingrate)\n") return nil}
  vin=$o1 sampr=$2 str=new String()
  if(numarg()>2) maxf=$3 else maxf=sampr/2
  if(numarg()>3) pord=$4 else pord=64
  p.vjnk = vin.to_python()
  p.vjnk = p.numpy.array(p.vjnk)
  sprint(str.s,"Pxx,F=ar(vjnk,rate=%g,order=%d,maxfreq=%g)",sampr,pord,maxf)
  nrnpython(str.s)
  nqp=new NQS("f","pow")
  nqp.v[0].from_python(p.F)
  nqp.v[1].from_python(p.Pxx)
  return nqp
}

//** pyspecgram(vec,samplingrate[,orows])
// this function calls python version of specgram, returns an nqs
obfunc pyspecgram () { local sampr,spc,i,j,sz,f,tt,orows,a localobj vin,str,nqp,ptmp,vtmp
  if(!INITPYWRAP) {printf("pyspecgram ERR0A: python.hoc not initialized properly\n") return nil}
  if(!nrnpython("from matplotlib.mlab import specgram")) {printf("pyspecgram ERR0B: could not import specgram from matplotlib.mlab!\n") return nil}  
  if(numarg()==0) {printf("pyspecgram(vec,samplingrate)\n") return nil}
  a=allocvecs(vtmp)
  vin=$o1 sampr=$2 str=new String()
  if(numarg()>2)orows=$3 else orows=1
  p.vjnk = vin.to_python()
  p.vjnk = p.numpy.array(p.vjnk)
  sprint(str.s,"[Pxx,freqs,tt]=specgram(vjnk,Fs=%g)",sampr)
  nrnpython(str.s)
  if(orows) {
    {nqp=new NQS("f","pow") nqp.odec("pow")}
    {sz=p.Pxx.shape[0] nqp.clear(sz)}
    for i=0,sz-1 {
      {vtmp.resize(0) vtmp.from_python(p.Pxx[i]) f=p.freqs[i]}
      nqp.append(f,vtmp)
    }
  } else {
    nqp=new NQS("f","pow","t")
    sz = p.Pxx.shape[0]
    nqp.clear(sz * p.Pxx.shape[1])
    for i=0,sz-1 {
      {vtmp.resize(0) vtmp.from_python(p.Pxx[i]) f=p.freqs[i]}
      for j=0,vtmp.size-1 nqp.append(f,vtmp.x(j),p.tt[j])
    }
  }
  dealloc(a)
  return nqp
}

//** pycsd(vec1,vec2,samplingrate)
// this function calls python version of csd (cross-spectral density)
// returns an nqs with csd -- csd is non-directional
obfunc pycsd () { local sampr,a localobj v1,v2,str,nqp
  if(!INITPYWRAP) {printf("pycsd ERR0A: python.hoc not initialized properly\n") return nil}
  if(!nrnpython("from matplotlib.mlab import csd")) {printf("pycsd ERR0B: could not import csd from matplotlib.mlab!\n") return nil}  
  if(numarg()==0) {printf("pycsd(vec,samplingrate)\n") return nil}
  v1=$o1 v2=$o2 sampr=$3 str=new String()
  {p.vjnk1=v1.to_python() p.vjnk1=p.numpy.array(p.vjnk1)}
  {p.vjnk2=v2.to_python() p.vjnk2=p.numpy.array(p.vjnk2)}
  sprint(str.s,"[Pxy,freqs]=csd(vjnk1,vjnk2,Fs=%g)",sampr)
  nrnpython(str.s)
  nqp=new NQS("f","pow")
  nqp.v[0].from_python(p.freqs)
  nqp.v[1].from_python(p.Pxy)
  return nqp
}

//** pypsd(vec,samplingrate[,NFFT])
// this function calls python version of psd (power-spectral density)
// returns an nqs with psd
obfunc pypsd () { local sampr,NFFT localobj v1,str,nqp
  if(!INITPYWRAP) {printf("pypsd ERR0A: python.hoc not initialized properly\n") return nil}
  if(!nrnpython("from matplotlib.mlab import psd")) {printf("pypsd ERR0B: could not import psd from matplotlib.mlab!\n") return nil}  
  // nrnpython("from matplotlib.mlab import window_none")
  if(numarg()==0) {printf("pypsd(vec,samplingrate)\n") return nil}
  v1=$o1 sampr=$2 str=new String() 
  {p.vjnk1=v1.to_python() p.vjnk1=p.numpy.array(p.vjnk1)}
  if(numarg()>2) NFFT=$3 else NFFT=v1.size
  if(sz%2==1) sz+=1
  sprint(str.s,"[Pxx,freqs]=psd(vjnk1,Fs=%g,NFFT=%d)",sampr,NFFT)
  nrnpython(str.s)
  nqp=new NQS("f","pow")
  nqp.v[0].from_python(p.freqs)
  nqp.v[1].from_python(p.Pxx)
  return nqp
}

//** pycohere(vec1,vec2,samplingrate) 
// this function calls python version of cohere (coherence is normalized csd btwn vec1, vec2)
// returns an nqs with coherence
obfunc pycohere () { local sampr,a localobj v1,v2,str,nqp
  if(!INITPYWRAP) {printf("pycohere ERR0A: python.hoc not initialized properly\n") return nil}
  if(!nrnpython("from matplotlib.mlab import cohere")) {printf("pycohere ERR0B: could not import cohere from matplotlib.mlab!\n") return nil}  
  if(numarg()==0) {printf("pycohere(vec1,vec2,samplingrate)\n") return nil}
  v1=$o1 v2=$o2 sampr=$3 str=new String()
  {p.vjnk1=v1.to_python() p.vjnk1=p.numpy.array(p.vjnk1)}
  {p.vjnk2=v2.to_python() p.vjnk2=p.numpy.array(p.vjnk2)}
  sprint(str.s,"[Pxy,freqs]=cohere(vjnk1,vjnk2,Fs=%g)",sampr)
  nrnpython(str.s)
  nqp=new NQS("f","coh")
  nqp.v[0].from_python(p.freqs)
  nqp.v[1].from_python(p.Pxy)
  return nqp
}

//* pypca(matrix) - does PCA on input matrix and returns scores (projections onto PCs)
// rows of the matrix are observations, columns are 'features' or 'dimensions'
obfunc pypca () { local r,c,a localobj inm,inmT,vin,vout,str,mout
  if(!INITPYWRAP) {printf("pypca ERR0A: python.hoc not initialized properly\n") return nil}
  if(!nrnpython("from princomp import PCA")) {printf("pypca ERR0B: could not import PCA!\n") return nil}  
  if(!nrnpython("import numpy as np")){printf("pypca ERR0C: could not import numpy as np!\n") return nil}
  str=new String2()
  if(numarg()<1) {printf("pypca(Vector,rows,cols)\n") return nil}
  a=allocvecs(vin,vout)
  {inm=$o1 r=inm.nrow c=inm.ncol}
  inmT = inm.transpose() // transpose since to_vector goes in column ordering
  {vin.resize(r*c) inmT.to_vector(vin)}
  p.vjnk = vin.to_python() // convert to python format
  sprint(str.s,"vjnk=np.resize(vjnk,(%d,%d))",r,c)
  if(!nrnpython(str.s)){printf("pypca ERR0D: could not run %s\n",str.s) dealloc(a) return nil}
  if(!nrnpython("mypca=PCA(vjnk)")){printf("pypca ERR0E: could not run PCA\n") dealloc(a) return nil}
  if(!nrnpython("score=mypca.Y")){printf("pypca ERR0F: could not set scores\n") dealloc(a) return nil}
  sprint(str.s,"score=np.resize(score,(%d,1))",r*c)
  if(!nrnpython(str.s)){printf("pypca ERR0E: could not run %s\n",str.s) dealloc(a) return nil}
  vout.from_python(p.score) // convert to a hoc Vector  
  mout=new Matrix(c,r)//output as a matrix. NB: c,r are reversed from original for following transpose
  mout.from_vector(vout)//from_vector uses column ordering
  mout = mout.transpose()//so need to transpose
  dealloc(a)
  return mout
}

//* pyspecck(vec,sampr[,maxf,win]) - call ck's spectrogram.py
// vec = time-series. sampr = sampling rate (Hz).
// maxf = max frequency. win = window size (seconds) for specgram chunks
// system call to spectrogram.py file to display a spectrogram, writes temp
// file and then deletes it...
func pyspecck () { local i,sampr,maxf,win localobj fp,vec,str
  vec=$o1 sampr=$2
  if(numarg()>2)maxf=$3 else maxf=sampr/2
  if(numarg()>3)win=$4 else win=1
  str=new String2()
  fp=new File()
  if(!fp.mktemp()){printf("pyspecck ERR0: couldn't make temp file!\n") return 0}
  str.s=fp.getname()
  fp.wopen(str.s)
  for i=0,vec.size-1 fp.printf("%g\n",vec.x(i))
  fp.close()  
  sprint(str.t,"/usr/site/nrniv/local/python/spectrogram.py %s %g %g %g",str.s,sampr,maxf,win)
  print str.t
  system(str.t)
  fp.unlink()
  return 1
}

//* pykstest(vec1,vec2) - perform a two-sample, two-sided kolmogorov-smirnov test
// and return the p-value. kstest checks if values in vec1,vec2 come from same distribution (null hypothesis)
// returns -1 on failure. uses scipy.stats.ks_2samp function
func pykstest () { localobj v1,v2
  if(!INITPYWRAP) {printf("pykstest ERR0A: python.hoc not initialized properly\n") return -1}
  {v1=$o1 v2=$o2}
  if(!nrnpython("from scipy.stats import ks_2samp")) return -1
  {p.v1=v1.to_python() p.v2=v2.to_python()}
  if(!nrnpython("(D,pval)=ks_2samp(v1,v2)")) return -1
  return p.pval  
}


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